Applied Statistics for Life Sciences

In science, statistical techniques provide a means of weighing quantitative evidence derived from observation and experimentation while accounting for uncertainty. This class aims to provide a hands-on introduction to common statistical methods used almost universally across the life sciences and a primer on statistical concepts. Examples emphasize applications with relevance to students’ majors, and students learn to perform simple analyses in R.

Read the [course syllabus] for more information.

Instructor: Trevor Ruiz (he/him) [email]

Office hours: TWR on Zoom [by appointment]

Class meetings: asynchronous online [canvas] [youtube] [piazza]

Final exam: in-person Thursday 7/24/25 10:10am – 1:00pm in 38-122

Week 1 (6/23/25)

Introduction to data, descriptive statistics, and foundations for statistical inference

• [reading] Vu and Harrington 1.1-1.5, 3.3.1-3.3.3, and 4.1-4.2

• [lecture 1] study design, data semantics

  • [slides] [video] [lab 1] [lab 1 solutions]

• [lecture 2] descriptive statistics

  • [slides] [video] [lab 2] [lab 2 solutions]

• [lecture 3] point estimation

  • [slides] [video] [lab 3] [lab 3 solutions]

• [lecture 4] interval estimation

  • [slides] [video] [lab 4] [lab 4 solutions]

• [HW1] due Thursday 6/26/25 11:59pm PDT

  • [assignment] [submission form] [submit corrections]

Week 2 (6/30/25)

Statistical inference for one, two, and many means

• [reading] Vu and Harrington 4.3, 5.3-5.5

• [lecture 5] inference for one population mean

  • [slides] [video] [lab 5] [lab 5 solutions]

• [lecture 6] differences in means between two populations

  • [slides] [video] [lab 6] [lab 6 solutions]

• [lecture 7] statistical power for two-sample inference

  • [slides] [video]

• [lecture 8] differences in means among many populations

  • [slides] [video] [lab 7] [lab 7 solutions]

• [HW2] due Thursday 7/3/25 11:59pm PDT

  • [assignment] [submission form] [submit corrections]

Week 3 (7/7/25)

Post-hoc inference; nonparametric methods; linear regression

• [reading] Vu and Harrington 6.1-6.5

• [lecture 9] post-hoc inference in ANOVA

  • [slides] [video] [lab 8] [lab 8 solutions]

• [lecture 10] nonparametric alternatives to $t$-tests and ANOVA

  • [slides] [video] [lab 9] [lab 9 solutions]

• [lecture 11] least squares estimation for simple linear regression

  • [slides] [video] [lab 10] [lab 10 solutions]

• [lecture 12] inference in regression

  • [slides] [video] [lab 11] [lab 11 solutions]

• [HW3] due Friday 7/11/25 11:59pm PDT

  • [assignment] [submission form] [submit corrections]

Week 4 (7/14/25)

Statistical inference for categorical data

• [reading] Vu and Harrington 8.1-8.4

• [lecture 13] one-sample inference for binomial data

  • [slides] [video]

• [lecture 14] one-sample inference for multinomial data

  • [slides] [video] [lab 12] [lab 12 solutions]

• [lecture 15] two-sample inference for binomial data

  • [slides] [video]

• [lecture 16] two-sample inference for multinomial data

  • [slides] [video] [lab 13] [lab 13 solutions]

• [HW4] due Friday 7/18/25 11:59pm PDT

  • [assignment] [submission form]

Week 5 (7/21/25)

Relative risk and odds ratios

• [reading] Vu and Harrington 8.5

• [lecture 17] relative risk

  • [slides] [video]

• [lecture 18] odds ratios

  • [slides] [video] [lab 14] [lab 14 solutions]

• [final exam] in person Thursday 7/24/25 10:10am – 1:00pm in 38-122

  • [study guide] [practice exam] [practice exam solutions]

Miscellany

Resources:

• how to do labs [video]
• how to submit homeworks [video]
• [rendering instructions] for downloading a PDF copy of your notebook
• [troubleshooting steps] for resolving rendering issues
• [tutoring resources]